Interacting hand pose estimation. Most works conduct interacting two-hand pose estimation by utilizing multi-view RGB images [1]}, [2]}, depth data [3]}, [4]}, [5]}, and tracking strategy [3]}, [7]}, [8]}. Only a few existing works have considered estimating 3D poses of two hands from a single RGB image, which is challenging due to severe occlusion and close interactions. Lin et al.  [9]} employed a synthetic egocentric hand dataset to learn to estimate two-hand poses from a single RGB image. Moon et al. proposed a large-scale interacting hand dataset, termed InterHand2.6M dataset [10]}, and designed the InterNet model to predict 2.5D hand poses.
Zhang et al.  [11]} designed a hand pose-aware attention module to address the self-similarity ambiguities and leveraged a context-aware cascaded refinement module to improve pose accuracy. Kim et al.  [12]} introduced an end-to-end trainable framework to jointly perform interacting hand pose estimation. Rong et al.  [13]} presented a two-stage framework to generate precise 3D hand poses and meshes with minimal collisions from monocular single RGB images. Fan et al.  [14]} proposed DIGIT (DIsambiGuating hands in InTeraction) to explicitly leverage the per-pixel probabilities to reduce the ambiguities caused by self-similarity of hands.
